Automotive Radar Basics & NXP Solutions
Transcript of Automotive Radar Basics & NXP Solutions
PUBLIC
System Architect ADAS & Connectivity, GC GSM
Apr. 21, 2020
Mingda Huang
Automotive Radar Basics
& NXP Solutions
PUBLIC 1COMPANY PUBLIC 1
• ADAS Overview
• Automotive Radar Basics
• NXP Radar Solutions
Agenda
ADAS
OVERVIEW
PUBLIC 3
Race to Self-Driving: Revolution and Evolution
Responsibility for safe operation Control of complete vehicle Control of steering Control of vehicle speed
LEVEL 1
Driver Assistance
LEVEL 2
Partial Automation
LEVEL 3
Conditional
Automation
• Adaptive cruise control
• Automatic braking
• Lane keeping
• Partial automated parking
• Traffic jam assistance
• Emergency brake with steer
• Semi autonomous:
−Highway chauffeur
• Human driver can regain
control
ADAS
Driver
Vehicle or
Driver
Vehicle
Driver
Vehicle
LEVEL 4
High Automation
• Autonomous driving in
some driving modes
• Valet parking
• Human driver may not
respond to request to
intervene
Driver
Vehicle
Self-Driving
LEVEL 5
Full Automation
• Fully autonomous under
all driving modes
• Human driver not
expected to respond to
request to intervene
Driver
Vehicle
SAE. (2014). AUTOMATED DRIVING LEVELS OF DRIVING AUTOMATION. SAE INTERNATIONAL STANDARD J3016.
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Automation Multiplies Sensors and Silicon Content
Camera77GHz Radar Lidar V2XFusion
Radar 1-3
Camera 1
Lidar
V2X
Fusion
Radar 4-6
Camera ≥4
Lidar 0-1
V2X 0-1
Fusion 1
Radar 6-10
Camera 6-8
Lidar 1-3
V2X 1-2
Fusion 1
units units units
1. Source: Strategy Analytics, NXP CMI
$100-$150Silicon Value
$600 Silicon Value
$900-$1200Silicon Value
In ProductionLevel 1/2
In DevelopmentLevel 3
New EntrantsLevel 4/5
AUTOMOTIVE
RADAR BASICS
PUBLIC 6
Radar Functionality• RAdio Detection And Ranging
• Measure:
− Distance to object
− Relative radial velocity
− Angle of arrival
− Radar cross section (RCS)
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Radar ApplicationsEvolves to 360°view with high-performance integration
Medium Range
RADARBlind Side Detection
Short range/
Medium range
RADARPark Assist
Cross-Traffic Alert
Junction Assist
Long Range
RADARAdaptive Cruise Control
Automatic Emergency Braking
Forward Collision Warning
TOMORROW
Higher
Resolution
PUBLIC
PUBLIC 8
Why Radar?
Competing and complementary technologies:
Camera, LIDAR, Ultrasonic.
Radar advantages:
• Robust against weather influences and
pollution
• Unaffected by light
• Direct distance and speed measurement
Radar Ultrasonic Camera Lidar
Distance Long Short Medium Long
Night Good Good Bad Good
Rain & Fog Good Affected Bad Bad
Classification Medium Bad Good Medium
Resolution Medium Bad Medium Good
Cost Medium Low Medium High
PUBLIC 9
mmWave Frequency Band
• Millimeter wave (mmWave) frequency band: 30 GHz – 300 GHz
• Wavelength of mmWave frequency: 10 mm – 1 mm
• Wavelength in free space = speed of light / frequency
Frequency (GHz) Wavelength in free space(mm)
2.4 125
24 – 24.25 12.5 – 12.4
76 – 77 3.95 – 3.9
77 – 81 3.9 – 3.7
𝜆0 = 𝑐0/𝑓
PUBLIC 10
Radar: Taking Safety to New Levels – Saving Lives
Detecting other carsSeeing pedestrians
& bicycles
Full 360°
surround view small & low-power sensors
Precise
Environmental Mape.g., curbstones & pylons in distance
+ + +
24GHz 77 GHz Multiple 77 GHz
Radar processing<1 GFlops
Advanced object detection15-20 GFlops
Imaging Radar>350 GFlops
RFCMOSSiGe
NXP full performance span
Bulky
High
Power
Low
Res
NXP Investment Focus
Small
Low
Power
High
Res
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Radar Equation
(RX power)
σ
R (distance)
G (Gain of antenna)
Pt (TX power)
Pr
(RCS)L(loss)
Radar Equation
𝑃𝑟 =𝑷𝒕𝐺𝑡𝐺𝑟𝜆
2𝜎
4𝜋 3𝑅4∙1
𝐿
𝑅𝑚𝑎𝑥 =4 𝑷𝒕𝐺𝑡𝐺𝑟𝜆
2𝜎
4𝜋 3𝑃𝑟𝑚𝑖𝑛𝐿
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FMCW Radar Range/Velocity Measurement
Fre
q (
GH
z)
TIMES0 , S1 , … , Sn , … , SR-1
Time of flight
FBeat
77
76
BW
Chirp
Fre
q (
GH
z)
TIME
77
76
Chirp 0 Chirp 1 Chirp D-1…
…
S0 , S1 , … , Sn , … , SR-1 S0 , S1 , … , Sn , … , SR-1 S0 , S1 , … , Sn , … , SR-1
fB_0 fB_1 fB_D-1
…
Gt
R
Pr
Ar
PtSignal Generation
Transmitter Chain
Receiver Chain
Down Conversion
Microprocessor- Control
- Signal Processing
- Object Detection
- Object Classification
LO
σ
v
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FMCW Radar Angle of Arrival Measurement
mD
Located far enough
to assume plane
wave
Tx antenna
Rx antenna
𝜆02
𝜆02
𝜆02
12 virtual receiver array 𝜆02
2𝜆02𝜆0
3 Tx array 4 Rx array
𝜆02
𝜆02
𝜆02
PUBLIC 14
Summary of FMCW Radar Range Velocity Angle Measurement
… …
PUBLIC 15
Radar Signal Processing
ReceiverSignal
analysisDetection
Signal
ConditioningTracking
• Antenna
• LNA
• Mixer
• HP/LPF
• AAF
• ADC
• Gain
• Window
• FFT
• Filter
• Power
• CFAR
• Clustering
• Kalman Filter
0 20 40 60 80 100 1200
1000
2000
3000
4000
5000
6000
7000
8000
Obj1=(d1, vr1,al1)
Obj2=(d2, vr2, al2)
HW EngineMMIC MCU
Signal Processing Enhanced Controller
Signal
Gen &
Tx
• Timing Engine
• Waveform
ageneration
• PA
• Antenna
NXP RADAR
SOLUTIONS
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Radar: NXP’s Unique Scalable Full System Solutions
• SiGe & RFCMOS transceiver portfolio
• Scalable processor families
• From simple sensor to imaging radar
• Full reference design, all interfaces aligned, few components needed
• Software development kit
• System functional safety checked
• Designed in with all top 10 OEMs…
• …while also growing with new players
• System solutions for China
RFCMOS
Transceiver
TEF810x
Radar Processor
S32R27/37
Power Management
FS84
CAN TJA104x
Ethernet TJA110x
Full 77 GHz System Solution Full Performance Span
Short Range
Mid
Range
Long
Range
4x NXP
Transceiver
Winning Across All Segments
Premium
OEMs
Full AutonomousNCAP
Mass
Market
+ =
Imaging
Radar
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Radar Software Enablement
Software & Enablement
✓ Automotive grade Radar SDK
✓ AUTOSAR safety MCAL and non-AUTOSAR
MCAL
✓ S32 Design Studio IDE support with 3rd party
plug-in support
✓ Compiler support (WindRiver, GreenHills)
✓ Extensive Debugger support
✓ Model based design in MATLAB™
Easy prototype
✓ S32 Design Studio IDE support
✓ 3rd party plug-in support
✓ Processor Expert based configuration
✓ Documented Source code and examples
✓ Eclipse or other IDEs
✓ Middleware stacks + FreeRTOS
Easy production
✓ Quality level: SPICE/CMMI compliant (Class B),
MISRA 2012 compliant
✓ Automotive-grade quality for low-level drivers
code, headers and middleware
✓ Multiple toolchains supported
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Enabling A/D Perception & Sensing Requirements in Radar
Detection &
Tracking
Mapping
Classification &
Segmentation
Localization
Resolve cluttered,
hidden objects & track
directionality
Static and Dynamic Object &
Free Space detection
(L4 functions)
3D Shapes (images) with
classification (Deep Learning)
Pedestrians, Cars,
Trucks, Motorcycles
Ego motion and pin-point
position via map correlation
or SLAM
for L3-4 vehicles
CONFIDENTIAL & PROPRIETARY 20
- Making Safe
& Secure Mobility a Reality
Solution
Portfolio
The most complete system
solutions for fastest time to
market and scalability.
Innovation
Power
In-house high performance
processing, security and mobile
eco-system capabilities.
Safe &
Secure
Zero defect methodology.
Leading with security and
functional safety.